Privacy protection in collective feedforward

ABSTRACT

Personal audio systems and methods are disclosed. A personal audio system includes a processor to generate a personal audio stream by processing an ambient audio stream in accordance with an active processing parameter set, a circular buffer memory to store a most recent snippet of the ambient audio stream, and an event detector to detect a trigger event. In response to detection of the trigger event, a controller may extract audio feature data from the most recent snippet of the ambient audio stream and transmit the audio feature data and associated metadata to a knowledgebase remote from the personal audio system.

RELATED APPLICATION INFORMATION

This patent is a continuation of application Ser. No. 15/617,271, filedJun. 8, 2017, entitled “Privacy Protection in Collective Feedforward,”which is a continuation of application Ser. No. 14/997,320, filed Jan.15, 2016, entitled “Privacy Protection in Collective Feedforward,” whichis a continuation-in-part of application Ser. No. 14/952,761, filed Nov.25, 2015, entitled “Processing Sound Using Collective Feedforward”. Thispatent is related to application Ser. No. 14/681,843, entitled “ActiveAcoustic Filter with Location-Based Filter Characteristics,” filed Apr.8, 2015; and application Ser. No. 14/819,298, entitled “Active AcousticFilter with Automatic Selection Of Filter Parameters Based on AmbientSound,” filed Aug. 5, 2015.

NOTICE OF COPYRIGHTS AND TRADE DRESS

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. This patent document may showand/or describe matter which is or may become trade dress of the owner.The copyright and trade dress owner has no objection to the facsimilereproduction by anyone of the patent disclosure as it appears in thePatent and Trademark Office patent files or records, but otherwisereserves all copyright and trade dress rights whatsoever.

BACKGROUND Field

This disclosure relates to digital active audio filters for use in alistener's ear to modify ambient sound to suit the listening preferencesof the listener.

Description of the Related Art

Humans' perception to sound varies with both frequency and soundpressure level (SPL). For example, humans do not perceive lower andhigher frequency sounds as well as they perceive sounds at midrangefrequencies between 500 Hz and 6,000 Hz. Further, human hearing is moreresponsive to sound at high frequencies compared to low frequencies.

There are many situations where a listener may desire attenuation ofambient sound at certain frequencies, while allowing ambient sound atother frequencies to reach their ears. For example, at a concert,concert goers might want to enjoy the music, but also be protected fromhigh levels of mid-range sound frequencies that cause damage to aperson's hearing. On an airplane, passengers might wish to block out theroar of the engine, but not conversation. At a sports event, fans mightdesire to hear the action of the game, but receive protection from theroar of the crowd. At a construction site, a worker may need to hearnearby sounds and voices for safety and to enable the construction tocontinue, but may wish to protect his or her ears from sudden, loudnoises of crashes or large moving equipment. Further, a user may wish toengage in conversation and other activities without being interrupted orimpaired by annoyance noises such as sounds of engines or motors, cryingbabies, and sirens. These are just a few common examples where peoplewish to hear some, but not all, of the sounds in their environment.

In addition to receiving protection from unpleasant or dangerously loudsound levels, listeners may wish to augment the ambient sound byamplification of certain frequencies, combining ambient sound with asecondary audio feed, equalization (modifying ambient sound by adjustingthe relative loudness of various frequencies), noise reduction, additionof white or pink noise to mask annoyances, echo cancellation, andaddition of echo or reverberation. For example, at a concert, audiencemembers may wish to attenuate certain frequencies of the music, butamplify other frequencies (e.g., the bass). People listening to music athome may wish to have a more “concert-like” experience by addingreverberation to the ambient sound. At a sports event, fans may wish toattenuate ambient crowd noise, but also receive an audio feed of asportscaster reporting on the event. Similarly, people at a mall maywish to attenuate the ambient noise, yet receive an audio feed ofadvertisements targeted to their location. These are just a few examplesof peoples' listening preferences.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a sound processing system.

FIG. 2 is block diagram of an active acoustic filter.

FIG. 3 is a block diagram of a personal computing device.

FIG. 4 is a functional block diagram of a portion of a personal audiosystem.

FIG. 5 is a block diagram of a sound knowledgebase.

FIG. 6 is a flow chart of a method for processing sound using collectivefeedforward.

FIG. 7 is a flow chart of a method for extracting features.

FIG. 8 is a flow chart of another method for processing sound usingcollective feedforward.

Throughout this description, elements appearing in figures are assignedthree-digit reference designators. An element not described inconjunction with a figure has the same characteristics and function as apreviously-described element having the same reference designator.

DETAILED DESCRIPTION

Description of Apparatus

Referring now to FIG. 1, a sound processing system 100 includes at leastone personal audio system 140 and a sound knowledgebase 150 within acloud 130. The sound processing system 100 may include a large pluralityof personal audio systems. In this context, the term “cloud” means anetwork and all devices that may be accessed by the personal audiosystem 140 via the network. The term “device” means an apparatus capableof communicating via a network or other digital communications link.Examples of devices include cellular phones, computing tablets, portablecomputers, desk-top computers, servers, network storage units, andperipheral devices. The cloud 130 may be a local area network, wide areanetwork, a virtual network, or some other form of network together withall devices connected to the network. The cloud 130 may be or includethe Internet. The term “knowledgebase” connotes a device that not onlystores data, but also learns and stores other knowledge derived from thedata. The sound knowledgebase 150 may include a database and one or moreservers. The sound knowledgebase 150 will be described in further detailduring the discussion of FIG. 5.

The personal audio system 140 includes left and right active acousticfilters 110L, 110R and a personal computing device 120. While thepersonal computing device 120 is shown in FIG. 1 as a smart phone, thepersonal computing device 120 may be a smart phone, a desktop computer,a mobile computer, a tablet computer, or any other computing device thatis capable of performing the processes described herein. The personalcomputing device 120 may include one or more processors and memoryconfigured to execute stored software instructions to perform theprocesses described herein.

The active acoustic filters 110L, 110R communicate with the personalcomputing device 120, such as via a first wireless communications link112. While only a single first wireless communications link 112 is shownin FIG. 1, each active acoustic filter 110L, 110R may communicate withthe personal computing device 120 via separate wireless communicationlinks. The first wireless communications link 112 may use alimited-range wireless communications protocol such as Bluetooth®,WiFi®, ZigBee®, or some other wireless Personal Area Network (PAN)protocol. Alternatively, there may be a direct connection from thepersonal computing device 120 to one of the active acoustic filters, andan indirect connection to the other active acoustic filter. The indirectconnection may be through the direct connection, wherein the directlyconnected active acoustic filter acts as a relay to the indirectlyconnected active acoustic filter.

The personal computing device 120 communicates with the cloud 130, forexample, via a second communications link 122. In particular, thepersonal computing device 120 may communicate with the soundknowledgebase 150 within the cloud 130 via the second communicationslink 122. The second communications link 122 may be a wired connectionor may be a wireless communications link using, for example, the WiFi®wireless communications protocol, a mobile telephone data protocol, oranother wireless communications protocol.

Optionally, the acoustic filters 110L, 110R may communicate directlywith the cloud 130 via a third wireless communications link 114. Thethird wireless communications link 114 may be an alternative to, or inaddition to, the first wireless communications link 112. The thirdwireless connection 114 may use, for example, the WiFi® wirelesscommunications protocol, or another wireless communications protocol.The acoustic filters 110L, 110R may communicate with each other via afourth wireless communications link (not shown). This fourth wirelesscommunication link may provide an indirect connection of one activeacoustic filter to the cloud 130 through the other active acousticfilter.

FIG. 2 is block diagram of an active acoustic filter 200, which isrepresentative of the active acoustic filter 110L and the activeacoustic filter 110R. The active acoustic filter 200 includes at leastone microphone 210, a preamplifier 215, an analog-to-digital (A/D)converter 220, a wireless interface 225, a processor 230, a memory 235,a digital-to-analog (D/A) converter 240, an amplifier 245, a speaker250, and a battery (not shown), all of which may be contained within ahousing 290. The active acoustic filter 200 receives ambient sound 205and outputs personal sound 255. In this context, the term “sound” refersto acoustic waves propagating in air. “Personal sound” means sound thathas been processed, modified, or tailored in accordance with a user'spersonal preferences. The term “audio” refers to an electronicrepresentation of sound, which may be an analog signal or digital data.

The housing 290 is configured to interface with a user's ear by fittingin, on, or over the user's ear such that the ambient sound 205 is mostlyexcluded from reaching the user's ear canal and the personal sound 255generated by the active acoustic filter is provided directly into theuser's ear canal.

The housing 290 has at least a first aperture 292 for accepting theambient sound 205 and a second aperture 294 to allow the personal sound255 to be output into the user's outer ear canal. The housing 290 mayhave more than one aperture for accepting ambient sound, each of whichmay be coupled to a separate microphone. The housing 290 may be, forexample, an earbud housing. The term “earbud” means an apparatusconfigured to fit, at least partially, within and be supported by auser's ear. An earbud housing typically has a portion that fits withinor against the user's outer ear canal. An earbud housing may have otherportions that fit within the concha or pinna of the user's ear.

The microphone 210 converts the ambient sound 205 into an electricalsignal that is amplified by preamplifier 215 and converted into anambient audio stream 222 by A/D converter 220. In this context, the term“stream” means a sequence of digital samples. The “ambient audio stream”is a sequence of digital samples representing the ambient sound receivedby the active acoustic filter 200. The ambient audio stream 222 isprocessed by processor 230 to provide a personal audio stream 232. Theprocessing performed by the processor 230 will be discussed in moredetail subsequently. The personal audio stream 232 is converted into ananalog signal by D/A converter 240. The analog signal output from D/Aconverter 240 is amplified by amplifier 245 and converted into personalsound 255 by speaker 250.

The microphone 210 may be one or more transducers for converting soundinto an electrical signal that is sufficiently compact for use withinthe housing 290. The preamplifier 215 is configured to amplify theelectrical signal output from the microphone 210 to a level compatiblewith the input of the A/D converter 220. The preamplifier 215 may beintegrated into the A/D converter 220, which, in turn, may be integratedwith the processor 230. In the situation where the active acousticfilter 200 contains more than one microphone, a separate preamplifiermay be provided for each microphone.

The A/D converter 220 digitizes the output from preamplifier 215, whichis to say converts the output from preamplifier 215 into a series ofdigital ambient audio samples at a rate at least twice the highestfrequency present in the ambient sound. For example, the A/D convertermay output the ambient audio stream 222 in the form of sequential audiosamples at rate of 40 kHz or higher. The resolution of the ambient audiostream 222 (i.e., the number of bits in each audio sample) may besufficient to minimize or avoid audible sampling noise in the processedoutput sound 255. For example, the A/D converter 220 may output anambient audio stream 222 having 12 or more bits of amplitude resolution.In the situation where the active acoustic filter 200 contains more thanone microphone with respective preamplifiers, the outputs from thepreamplifiers may be digitized separately, or the outputs of some or allof the preamplifiers may be combined prior to digitization.

The wireless interface 225 provides digital acoustic filter 200 with aconnection to one or more wireless networks 295 using a limited-rangewireless communications protocol such as Bluetooth®, WiFi®, ZigBee®, orother wireless personal area network protocol. The wireless interface225 may be used to receive data such as parameters for use by theprocessor 230 in processing the ambient audio stream 222 to produce thepersonal audio stream 232. The wireless interface 225 may be used toreceive a secondary audio feed. The wireless interface 225 may be usedto export the personal audio stream 232, which is to say transmit thepersonal audio stream 232 to a device external to the active acousticfilter 200. The external device may then, for example, store and/orpublish the personal audio stream, for example via social media.

The processor 230 may include one or more processor devices such as amicrocontroller, a microprocessor, and/or a digital signal processor.The processor 230 can include and/or be coupled to the memory 235. Thememory 235 may store software programs, which may include an operatingsystem, for execution by the processor 230. The memory 235 may alsostore data for use by the processor 230. The data stored in the memory235 may include, for example, digital sound samples and intermediateresults of processes performed on the ambient audio stream 222. The datastored in the memory 235 may also include a user's listeningpreferences, and/or rules and parameters for applying particularprocesses to convert the ambient audio stream 222 into the personalaudio stream 232. The memory 235 may include a combination of read-onlymemory, flash memory, and static or dynamic random access memory.

The D/A converter 240 converts the personal audio stream 232 from theprocessor 230 into an analog signal. The processor 230 outputs thepersonal audio stream 232 as a series of samples typically, but notnecessarily, at the same rate as the ambient audio stream 222 isgenerated by the A/D converter 220. The analog signal output from theD/A converter 240 is amplified by the amplifier 245 and converted intopersonal sound 255 by the speaker 250. The amplifier 245 may beintegrated into the D/A converter 240, which, in turn, may be integratedwith the processor 230. The speaker 250 can be any transducer forconverting an electrical signal into sound that is suitably sized foruse within the housing 290.

A battery or other power supply (not shown) provides power to variouselements of the active acoustic filter 200. The battery may be, forexample, a zinc-air battery, a lithium ion battery, a lithium polymerbattery, a nickel cadmium battery, or a battery using some othertechnology.

The depiction in FIG. 2 of the active acoustic filter 200 as a set offunctional blocks or elements does not imply any corresponding physicalseparation or demarcation. All or portions of one or more functionalelements may be located within a common circuit device or module. Any ofthe functional elements may be divided between two or more circuitdevices or modules. For example, all or portions of theanalog-to-digital (A/D) converter 220, the processor 230, the memory235, the analog signal by digital-to-analog (D/A) converter 240, theamplifier 245, and the wireless interface 225 may be contained within acommon signal processor circuit device.

FIG. 3 is a block diagram of an exemplary personal computing device 300,which may be the personal computing device 120. As shown in FIG. 3, thepersonal computing device 300 includes a processor 310, memory 320, auser interface 330, a communications interface 340, and an audiointerface 350. Some of these elements may or might not be present,depending on the implementation. Further, although these elements areshown independently of one another, each may, in some cases, beintegrated into another.

The processor 310 may be or include one or more microprocessors,microcontrollers, digital signal processors, application specificintegrated circuits (ASICs), or a system-on-a-chip (SOCs). The memory320 may include a combination of volatile and/or non-volatile memoryincluding read-only memory (ROM), static, dynamic, and/ormagnetoresistive random access memory (SRAM, DRM, MRAM, respectively),and nonvolatile writable memory such as flash memory.

The memory 320 may store software programs and routines for execution bythe processor. These stored software programs may include an operatingsystem such as the Apple® MacOS or IOS operating systems or the Android®operating system. The operating system may include functions to supportthe communications interface 340, such as protocol stacks,coding/decoding, compression/decompression, and encryption/decryption.The stored software programs may include an application or “app” tocause the personal computing device to perform portions of the processesand functions described herein.

The user interface 330 may include a display and one or more inputdevices such as a touch screen.

The communications interface 340 includes at least one interface forwireless communication with external devices. The communicationsinterface 340 may include one or more of a cellular telephone networkinterface 342, a wireless local area network (LAN) interface 344, and/ora wireless personal area network (PAN) interface 336. The cellulartelephone network interface 342 may use one or more cellular dataprotocols. The wireless LAN interface 344 may use the WiFi® wirelesscommunication protocol or another wireless local area network protocol.The wireless PAN interface 346 may use a limited-range wirelesscommunication protocol such as Bluetooth®, WiFi®, ZigBee®, or some otherpublic or proprietary wireless personal area network protocol. When thepersonal computing device 300 is deployed as part of a personal audiosystem, such as the personal audio system 140, the wireless PANinterface 346 may be used to communicate with the active acoustic filterdevices 110L, 110R. The cellular telephone network interface 342 and/orthe wireless LAN interface 344 may be used to communicate with the cloud130.

The communications interface 340 may include radio-frequency circuits,analog circuits, digital circuits, one or more antennas, and otherhardware, firmware, and software necessary for communicating withexternal devices. The communications interface 340 may include one ormore processors to perform functions such as coding/decoding,compression/decompression, and encryption/decryption as necessary forcommunicating with external devices using selected communicationsprotocols. The communications interface 340 may rely on the processor310 to perform some or all of these function in whole or in part.

The audio interface 350 may be configured to both input and outputsound. The audio interface 350 may include more or more microphones,preamplifiers and A/D converters that perform similar functions as themicrophone 210, preamplifier 215 and A/D converter 220 of the activeacoustic filter 200. The audio interface 350 may include more or moreD/A converters, amplifiers, and speakers that perform similar functionsas the D/A converter 240, amplifier 245 and speaker 250 of the activeacoustic filter 200.

The personal computing device 300 may be configured to performgeo-location, which is to say to determine its own location.Geo-location may be performed, for example, using a Global PositioningSystem (GPS) receiver or by some other method.

FIG. 4 shows a functional block diagram of a portion of an exemplarypersonal audio system 400, which may be the personal audio system 140.The personal audio system 400 includes one or two active acousticfilters, such as the active acoustic filters 110L, 110R, and a personalcomputing device, such as the personal computing device 120. Thefunctional blocks shown in FIG. 4 may be implemented in hardware, bysoftware running on one or more processors, or by a combination ofhardware and software. The functional blocks shown in FIG. 4 may beimplemented within the personal computing device, or within one or bothactive acoustic filters, or may be distributed between the personalcomputing device and the active acoustic filters.

The personal audio system 400 includes an audio processor 410, acontroller 420, a dataset memory 430, an audio snippet memory 440, auser interface 450 and a geo-locator 460. The audio processor 410 and/orthe controller 420 may include their own memory, which is not shown, forstoring program instructions, intermediate results, and other data.

The audio processor 410 may be or include one or more microprocessors,microcontrollers, digital signal processors, application specificintegrated circuits (ASICs), or a system-on-a-chip (SOCs). The audioprocessor 410 may be located within an active acoustic filter, withinthe personal computing device, or may be distributed between a personalcomputing device and one or two active acoustic filters.

The audio processor 410 receives and processes a digital ambient audiostream, such as the ambient audio stream 222, to provide a personalaudio stream, such as the personal audio stream 232. The audio processor410 may perform processes including filtering, equalization,compression, limiting, and/or other processes. Filtering may includehigh-pass, low-pass, band-pass, and band-reject filtering. Equalizationmay include dividing the ambient sound into a plurality of frequencybands and subjecting each of the bands to a respective attenuation orgain. Equalization may be combined with filtering, such as a narrowband-reject filter to suppress a particular objectionable component ofthe ambient sound. Compression may be used to alter the dynamic range ofthe ambient sound such that louder sounds are attenuated more thansofter sounds. Compression may be combined with filtering or withequalization such that louder frequency bands are attenuated more thansofter frequency bands. Limiting may be used to attenuate louder soundsto a predetermined loudness level without attenuating softer sounds.Limiting may be combined with filtering or with equalization such thatlouder frequency bands are attenuated to a defined level while softerfrequency bands are not attenuated or attenuated by a smaller amount.

The audio processor 410 may also add echo or reverberation to theambient audio stream. The audio processor 410 may also detect and cancelan echo in the ambient audio stream. The audio processor 410 may furtherperform noise reduction processing.

The audio processor 410 may receive a secondary audio stream. The audioprocessor 410 may incorporate the secondary audio stream into thepersonal audio stream. The secondary audio stream may be added to theambient audio stream before processing, after all processing of theambient audio stream is performed, or at an intermediate stage in theprocessing of the ambient audio stream. The secondary audio stream mightnot be processed, or may be processed in the same manner as or in adifferent manner than the ambient audio stream.

The audio processor 410 may process the ambient audio stream, andoptionally the secondary audio stream, in accordance with an activeprocessing parameter set 425. The active processing parameter set 425may define the type and degree of one or more processes to be performedon the ambient audio stream and, when desired, the secondary audiostream. The active processing parameter set may include numericalparameters, filter models, software instructions, and other informationand data to cause the audio processor to perform desired processes onthe ambient audio stream. The extent and format of the information anddata within active processing parameter set 425 may vary depending onthe type of processing to be performed. For example, the activeprocessing parameter set 425 may define filtering by a low pass filterwith a particular cut-off frequency (the frequency at which the filterstart to attenuate) and slope (the rate of change of attenuation withfrequency) and/or compression using a particular function (e.g.logarithmic). For further example, the active processing parameter set425 may define the plurality of frequency bands for equalization andprovide a respective attenuation or gain for each frequency band. In yetanother example, the processing parameters may define a delay time andrelative amplitude of an echo to be added to the digitized ambientsound.

The audio processor 410 may receive the active processing parameter set425 from the controller 420. The controller 420, in turn, may obtain theactive processing parameter set 425 from the user via the user interface450, from the cloud (e.g. from the sound knowledgebase 150 or anotherdevice within the cloud), or from the dataset memory 430 within thepersonal audio system 400.

The dataset memory 430 may store one or more processing parameter sets432, which may include a copy of the active processing parameter set425. The dataset memory 430 may store dozens or hundreds or an evenlarger number of processing parameter sets 432. Each processingparameter set 432 may be associated with at least one indicator, wherean “indicator” is data indicating conditions or circumstances where theassociated processing parameter set 432 is appropriate for selection asthe active processing parameter set 425. The indicators associated witheach processing parameter set 432 may include one or more of a location434, an ambient sound profile 436, and a context 438. The combination ofa processing parameter set and its associated indicators is considered a“dataset”.

Locations 434 may be associated with none, some, or all of theprocessing parameter sets 432 and stored in the dataset memory 430. Eachlocation 434 defines a geographic position or limited geographic areawhere the associated set of processing parameters 432 is appropriate. Ageographic position may be defined, for example, by a street address,longitude and latitude coordinates, GPS coordinates, or in some othermanner. A geographic position may include fine-grained information suchas a floor or room number in a building. A limited geographic area maybe defined, for example, by a center point and a radius, by a pair ofcoordinates identifying diagonal corners of a rectangular area, by aseries of coordinates identifying vertices of a polygon, or in someother manner.

Ambient sound profiles 436 may be associated with none, some, or all ofthe processing parameter sets 432 and stored in the dataset memory 430.Each ambient sound profile 436 defines features and characteristics ofan ambient sound environment in which the associated processingparameter set 432 is appropriate. Each ambient sound profile 436 maydefine the features and characteristics of the ambient sound environmentby a finite number of numerical values. For example, an ambient profilemay include numerical values for some or all of an overall loudnesslevel, a normalized or absolute loudness of predetermined frequencybands, a spectral envelope shape, spectrographic features such as risingor falling pitch, frequencies and normalized or absolute loudness levelsof dominant narrow-band sounds, an indicator of the presence or absenceof odd and/or even harmonics, a normalized or absolute loudness ofnoise, a low frequency periodicity (e.g. the “beat” when the ambientsound includes music), and numerical values quantifying other featuresand/or characteristics.

Contexts 438 may be associated with none, some, or all of the processingparameter sets 432 and stored in the dataset memory 430. Each context438 is a descriptive name of an environment or situation in which theassociated processing parameter set 432 is appropriate. Examples ofcontexts include “airplane cabin,” “subway,” “urban street,” “siren,”and “crying baby.” A context is not necessarily associated with aspecific geographic location, but may be associated with a genericlocation such as, for example, “airplane,” “subway,” and “urban street.”A context may be associated with a type of ambient sound such as, forexample, “siren,” “crying baby,” and “rock concert.” A context may beassociated with one or more sets of processing parameters. When acontext is associated with multiple processing parameter sets 432,selection of a particular processing parameter set may be based onlocation or ambient sound profile. For example, “siren” may beassociated with a first set of processing parameters for locations inthe United States and a different set of processing parameters forlocations in Europe.

The controller 420 may select a processing parameter set 432 for use asthe active processing parameter set 425 based on location, ambient soundprofile, context, or a combination thereof. Retrieval of a processingparameter set 432 may be requested by the user via the user interface450. Alternatively or additionally, retrieval of a processing parameterset 432 may be initiated automatically by the controller 420.

For example, the controller 420 may include a profile developer 422 toanalyze the ambient audio stream to develop a current ambient soundprofile. In this case, the controller 420 compares the current ambientsound profile with a stored prior ambient sound profile. When thecurrent ambient sound profile is judged, according to firstpredetermined criteria, to be substantially different from the priorambient sound profile, the controller 420 initiates retrieval of a newprocessing parameter set 432.

The personal audio system 400 may contain a geo-locator 460. Thegeo-locator 460 determines a geographic location of the personal audiosystem 400 using GPS, cell tower triangulation, or some other method. Asdescribed in co-pending application Ser. No. 14/681,843, entitled“Active Acoustic Filter with Location-Based Filter Characteristics,” thecontroller 420 may compare the geographic location of the personal audiosystem 400, as determined by the geo-location 460, with locationindicators 434 stored in the dataset memory 430. When one of thelocation indicators 434 matches, according to second predeterminedcriteria, the geographic location of the personal audio system 400, theassociated processing parameter set 432 may be retrieved and provided tothe audio processor 410 as the active processing parameter set 425.

As described in co-pending application Ser. No. 14/819,298, entitled“Active Acoustic Filter with Automatic Selection of Filter ParametersBased on Ambient Sound,” the controller may select a processingparameter set 432 based on the ambient sound. The controller 420 maycompare the profile of the ambient sound, as determined by the profiledeveloper 422, with profile indicators 436 stored in the dataset memory430. When one of the profile indicators 436 matches, according to thirdpredetermined criteria, the profile of the ambient sound, the associatedprocessing parameter set 432 may be retrieved and provided to the audioprocessor 410 as the active processing parameter set 425.

In some circumstances, for example upon user request or when a matchinglocation or profile is not found in the dataset memory 430, thecontroller 420 may present a list of the contexts 438 on a userinterface 450. A user may then manually select one of the listedcontexts and the associated processing parameter set 432 may beretrieved and provided to the audio processor 410 as the activeprocessing parameter set 425. For example, assuming the user interfaceincludes a display with a touch screen, the list of contexts may bedisplayed on the user interface as array of soft buttons. The user maythen select one of the contexts by pressing the associated button.

Datasets (i.e., processing parameter sets 432 and associated indicators434, 436, 438) may be entered into the dataset memory 430 in severalways. Datasets may have been stored in the dataset memory 430 duringmanufacture of the personal audio system 400. Datasets may have beenstored in the dataset memory 430 during installation of an applicationor “app” on the personal computing device that is a portion of thepersonal audio system.

Additional datasets stored in the dataset memory 430 may have beencreated by the user of the personal audio system 400. For example, anapplication running on the personal computing device may present agraphical user interface through which the user can select and controlparameters to edit an existing processing parameter set and/or to createa new processing parameter set. In either case, the edited or newprocessing parameter set may be saved in the dataset memory 430 inassociation with one or more of a current ambient sound profile providedby the profile developer 422, a location of the personal audio system400 provided by the geo-locator 460, and a context or name entered bythe user via the user interface 450. The edited or new processingparameter set to be saved in the dataset memory 430 automatically or inresponse to a specific user command.

Datasets may be developed by third parties and made accessible to theuser of the personal audio system 400, for example, via a network.

Further, datasets may be downloaded from the cloud, such as from thesound knowledgebase 150 in the cloud 130, and stored in the datasetmemory 430. For example, newly available or revised processing parametersets 432 and associated indicators 434, 436, 438 may be pushed from thecloud to the personal audio system 400 automatically. Newly available orrevised processing parameter sets 432 and associated indicators 434,436, 438 may be downloaded from the cloud by the personal audio system400 at periodic intervals. Newly available or revised processingparameter sets 432 and associated indicators 434, 436, 438 may bedownloaded from the cloud by the personal audio system 400 in responseto a request from a user.

To support development of new and/or revised processing parameter sets,the personal audio system may upload information, such as to the soundknowledgebase 150 in the cloud 130.

The personal audio system may contain an audio snippet memory 440. Theaudio snippet memory 440 may be, for example, a revolving or circularbuffer memory having a fixed size where the newest data overwrites theoldest data such that, at any given instant, the buffer memory containsa predetermined amount of the most recently stored data. The audiosnippet memory 440 may store, for example, the most recent audio streamdata for a period of 2 seconds, 5 seconds, 10 seconds, 30 seconds, orsome other period.

The audio snippet memory 440 may store a “most recent portion” of anaudio stream, where the “most recent portion” is the time periodimmediately preceding the current time. The audio snippet memory 440 maystore the most recent portion of the ambient audio stream input to theaudio processor 410 (as shown in FIG. 4), in which case the audiosnippet memory 440 may be located within one or both of the activeacoustic filters of the personal audio system. The audio snippet memory440 may store the most recent portion of an audio stream derived fromthe audio interface 350 in the personal computing device of the personalaudio system, in which case the audio snippet memory may be locatedwithin the personal computing device 120. In either case, the durationof the most recent portion of the audio stream stored in the audiosnippet memory 440 may be sufficient to capture very low frequencyvariations in the ambient sound such as, for example, periodic frequencymodulation of a siren or interruptions in a baby's crying when the babyinhales.

The personal audio system my include an event detector 424 to detecttrigger events, which is to say events that trigger uploading thecontent of the audio snippet memory and associated metadata to theremote device. The event detector 424 may be part of, or coupled to, thecontroller 420. The event detector 424 may detect events that indicateor cause a change in the active processing parameter set 425 used by theaudio processor 410 to process the ambient audio stream. Examples ofsuch events detected by the event detector include the user enteringcommands via the user interface 450 to modify the active processingparameter set 425 or to create a new processing parameter set; the userentering a command via the user interface 450 to save a modified or newprocessing parameter set in the dataset memory 430; automatic retrieval,based on location or ambient sound profile, of a selected processingparameter set from the dataset memory 430 for use as the activeprocessing parameter set; and user selection, for example from a list orarray of buttons presented on the user interface 450, of a selectedprocessing parameter set from the dataset memory 430 for use as theactive processing parameter set. Such events may be precipitated, forexample, by a change in the ambient sound environment or by userdissatisfaction with the sound of the personal audio stream obtainedwith the previously-used active processing parameter set.

application Ser. No. 14/952,761, “Processing Sound Using CollectiveFeedforward”, describes a personal audio system that, in response to atrigger event, uploads a most recent audio snippet (i.e., the content ofan audio snippet memory) and associated metadata to a remote device.Uploading the most recent audio snippet allows the remote device toperform various analyses to determine the characteristics of the ambientsound immediately prior to the event. However, the most recent audiosnippet may contain speech of the user of the personal audio system orother persons. Thus uploading the most recent audio snippet may raiseprivacy concerns.

The personal audio system 400 may include a feature extractor 426 toextract feature data from the most recent audio snippet stored in theaudio snippet memory 440. Feature data that may be extracted from themost recent audio snippet include, for example, data such as means(arithmetic, geometric, harmonic), centroid, variance, standarddeviation, spectral skew, kurtosis, spectral envelope shape, spectralrolloff, spread, flatness, spectral flux, Mel frequency cepstralcoefficients, pitch, tonal power ratio, harmonic-to-average power ratio,maximum of autocorrelation function, zero crossing rate, RMS power, peakpower, crest factor, and/or amplitude/power envelope includingestimation of attack/decay rates.

Upon detection of an event by the event detector 424, the featureextractor 426 may extract feature data from the most recent audiosnippet stored in the audio snippet memory. The type of audiofeaturization to apply, and the type and amount of feature dataextracted from the most recent audio snippet may depend on thecharacteristics of the stored audio. Some feature data may be extractedfrom the entire content of the audio snippet memory 440 and otherfeature data may be extracted from multiple consecutive time slices ofthe audio data stored in the audio snippet memory 440. The extractedfeature data may then be transmitted to a remote device such as thesound knowledge base in the cloud 130. The feature data transmitted tothe remote device may be configured to allow analysis of the ambientsound immediately preceding the event, but not allow reconstruction ofspeech or recognition of the user or other speakers.

Metadata may be transmitted along with the extracted feature data. Thetransmitted metadata may include a location of the personal audio system400, which may be provided by the geo-locator 460. When the triggerevent was a user-initiated or automatic retrieval of a selectedprocessing parameter set from the parameter memory, the transmittedmetadata may include an identifier of the selected processing parameterset and/or the complete selected processing parameter set. When thetrigger event was the user modifying a processing parameter set orcreating a new processing parameter set, the transmitted metadata mayinclude the modified or new processing parameter set. Further, the usermay be prompted or required to enter, such as via the user interface450, a context, descriptor, or other tag to be associated with theextracted feature data and transmitted. To preserve user privacy, thetransmitted metadata may exclude information that identifies the user orthe user's device.

FIG. 5 is a functional block diagram of an exemplary sound knowledgebase500, which may be the sound knowledgebase 150 within the soundprocessing system 100. The sound knowledgebase 500 includes a processor510 coupled to a memory/storage 520 and a communications interface 540.These functions may be implemented, for example, in a single servercomputer or by one or more real or virtual servers within the cloud.

The processor 510 may be or include one or more microprocessors,microcontrollers, digital signal processors, application specificintegrated circuits (ASICs), or a system-on-a-chip (SOCs). Thememory/storage 520 may include a combination of volatile and/ornon-volatile memory. The memory/storage 520 may include one or morestorage devices that store data on fixed or removable storage media. Theterm “storage media” means a physical object adapted for storing data,which excludes transitory media such as propagating signals or waves.Examples of storage media include magnetic discs and optical discs.

The communications interface 540 includes at least one interface forwired or wireless communications with external devices including aplurality of personal audio systems.

The memory/storage 520 may store a database 522 having a plurality ofrecords. Each record in the database 522 may include a set of audiofeature data and associated metadata received from one of a plurality ofpersonal audio systems, such as the personal audio system 400, via thecommunication interface 540. The memory/storage 520 may also storesoftware programs and routines for execution by the processor. Thesestored software programs may include an operating system. The operatingsystem may include functions to support the communications interface540, such as protocol stacks, coding/decoding,compression/decompression, and encryption/decryption. The storedsoftware programs may include a database application (also not shown) tomanage the database 522.

The stored software programs in the memory/storage 520 may include afeature data analysis application 524 to analyze audio feature datareceived from the plurality of personal audio systems. The feature dataanalysis application 524 may, for example, extract or develop additionaldata representing the characteristics and features of the ambient soundat the personal audio system that provided the audio feature data.Additional data extracted or developed by feature data analysisapplication 524 may be stored in the database 522 as part of the recordcontaining the corresponding audio feature data and metadata.

The stored software programs may include a parameter set learningapplication 526 to learn revised and/or new processing parameter setsfrom the audio feature data, additional data, and metadata stored in thedatabase 522. The parameter set learning application 526 may use avariety of analytical techniques to learn revised and/or new processingparameter sets. These analytical techniques may be applied to numericaland statistical analysis of audio feature data, additional data, andnumerical metadata such as location, date, and time metadata. Theseanalytical techniques may include, for further example, semanticanalysis of tags, descriptors, contexts, and other non-numericalmetadata. Further, the parameter set learning application 526 may useknown machine learning techniques such as neural nets, fuzzy logic,adaptive neuro-fuzzy inference systems, or combinations of these andother machine learning methodologies to learn revised and/or newprocessing parameter sets.

As an example of a learning process that may be performed by theparameter set learning application 526, the records in the database 522may be sorted into a plurality of clusters based according to audiofeature data, location, tag or descriptor or some other factor. Some orall of these clusters may optionally be sorted into sub-clusters basedon another factor. When records are sorted into clusters or sub-clustersbased on non-numerical metadata (e.g., tags or descriptors) semanticanalysis may be used to combine like metadata into a manageable numberof clusters or sub-clusters. A consensus processing parameter set maythen be developed for each cluster or sub-cluster. For example, clearoutliers may be discarded and the consensus processing parameter set maybe formed from the medians or means of processing parameters within theremaining processing parameter sets.

The memory/storage 520 may include a master parameter memory 528 tostore all processing parameter sets and associated indicators currentlyused within the sound processing system 100. New or revised processingparameter sets developed by the parameter set learning application 526may be stored in the master parameter memory 528. Some or all of theprocessing parameter sets stored in the master parameter memory 528 maybe downloaded via the communications interface 540 to each of theplurality of personal audio systems in the sound processing system 100.For example, new or recently revised processing parameter sets may bepushed to some or all of the personal audio systems as available.Processing parameters sets, including new and revised processingparameter sets may be downloaded to some or all of the personal audiosystems at periodic intervals. Processing parameters sets, including newand revised processing parameter sets may be downloaded upon requestfrom individual personal audio systems.

Description of Processes

FIG. 6 shows flow charts of methods 600 and 700 for processing soundusing collective feedforward. The methods 600 and 700 may be performedby a sound processing system, such as the sound processing system 100(FIG. 1). The sound processing system may include a large plurality ofpersonal audio systems, each having characteristics as akin to thepersonal audio system 140 (FIG. 1). The method 700 will, in most cases,produce better results with scale, so having more personal audio systemswill usually be better. Thus, having thousands of personal audiosystems, or even millions, may produce superior benefits.

The method 600 may be performed by each personal audio systemconcurrently but not necessarily synchronously. The method 700 may beperformed by the sound knowledgebase concurrently with the method 600.All or portions of the methods 600 and 700 may be performed by hardware,by software running on one or more processors, or by a combination ofhardware and software. All or portions of the method 600 may beperformed by an active acoustic filter, such as the active acousticfilter 200, or may be distributed between an active acoustic filter anda personal computing device, such as the personal computing device 120.Although shown as a series of sequential actions for ease of discussion,the actions from 710 to 750 may occur continuously and simultaneously,and that the actions from 610 to 660 may be performed concurrently bythe plurality of personal audio systems. Further, in FIG. 6, processflow is indicated solid arrows and information flow is indicated bydashed arrows.

The method 600 may start at 605 and run continuously until stopped (notshown). At 610, one or more processing parameter sets and associatedindicators may be stored in a parameter memory, such as the datasetmemory 430, within the personal audio system. Initially, one or moreprocessing parameter sets may be stored in the personal audio systemduring manufacture or during installation of a personal audio systemapplication on a personal computing device. Subsequently, new and/orrevised processing parameter sets and associated indicators may bereceived from the sound knowledgebase.

At 620, an ambient audio stream derived from ambient sound may beprocessed in accordance with an active processing parameter set selectedfrom the processing parameters sets stored at 610. Processes that may beperformed at 620 were previous described. Concurrently with processingthe ambient audio stream at 620, a most recent portion of the ambientaudio stream may be stored in a snippet memory at 630, also aspreviously described.

At 640, a determination may be made whether or not a trigger event hasoccurred. Trigger events were previously described. When a determinationis made at 640 that a trigger event has not occurred (“no” at 640), theprocessing at 620 and storing at 630 may continue. When a determinationis made at 640 that a trigger event has occurred (“yes” at 640), aprocessing parameter set may be stored or retrieved at 650 asappropriate. At 650, a current processing parameter set, for example, asdefined or edited by a user, may be stored in dataset memory 430. At650, a previously stored processing parameter set may be retrieved fromthe dataset memory 430, either in repose to a user action orautomatically, for example in response to a change in the ambient soundor user location.

At 660, feature data may be extracted from the most recent audiosnippet. The audio snippet memory may be located within one or bothactive acoustic filters, or within a personal computing device, or maybe distributed between the active acoustic filters and the personalcomputing device. Feature extraction may be performed by a processorwithin one or both active acoustic filters, a processor within thepersonal computing device, or may be distributed between the activeacoustic filters and the personal computing device. Audio snippet datastored in one or both active acoustic filters may be transmitted to thepersonal computing device prior to feature extraction.

As previously described, data regarding a large number of differentfeatures can be extracted from an audio signal. Extracting all of thepossible feature data from the most recent audio snippet may present anunreasonable or undesirable burden on the processor(s) within a personalaudio system. Further only a subset of the possible feature data may berelevant to any given ambient sound environment.

To reduce the processing burden, an optional process 800, shown in FIG.7, may be employed at 660 to preselect relevant feature data before someor all of the feature data is extracted. At 810, a preliminary analysisis performed on the most recent audio snippet. Alternately oradditionally, at 820, a query may be sent, via a user interface, to auser to identify a sound (e.g. gunshot, breaking glass, automobilecrash, etc.) captured in the most recent audio snippet. At 830, a subsetof the possible audio feature data is selected as particularly relevantto the most recent audio snippet. The relevant audio feature data may beselected based on the results of the preliminary analysis at 810, a userresponse to the query at 820, and/or the type of event detected at 640(FIG. 6). At 840 the relevant audio feature data is extracted from themost recent audio snippet and subsequently transmitted to theknowledgebase.

The type of features and algorithms for extracting features at 840 andcriteria for selecting relevant audio feature data at 830 may evolveover time. For example, the type of features, algorithms for extractingfeatures, and criteria for selecting relevant audio feature data may bedefined by the knowledgebase and transmitted to the personal audiosystem as updates to firmware and/or software for the processors in theactive audio filters and/or the personal computing device.

Referring once again to FIG. 6, at 670 the extracted feature data andassociated metadata may be transmitted or uploaded to the soundknowledgebase. The uploaded metadata may include a location of thepersonal audio system provided by a geo-locator within the personalaudio system. When the trigger event was a user-initiated or automaticretrieval of a selected processing parameter set from the parametermemory, the uploaded metadata may include an identifier of the selectedprocessing parameter set and/or the actual selected processing parameterset. When the trigger event was the user modifying the active processingparameter or creating a new processing parameter set, the uploadedmetadata may include the modified or new processing parameter set.Further, the user may be prompted to enter a context, descriptor, orother tag to be associated with the modified or new processing parameterset and uploaded. The process 600 may then return to 620 and continuecyclically until stopped.

At 710, the sound knowledgebase receives the feature data and associatedmetadata transmitted at 670 and may receive additional feature data andmetadata from other personal audio systems. Analysis may be performed onthe received feature data at 720. The audio analysis at 720 may developadditional data about the features and characteristics of the ambientaudio at the personal audio system. The additional data developed by theaudio analysis at 720 may be stored in a database at 730 in associationwith the corresponding audio feature data and metadata received at 710.

At 740, machine learning techniques may be applied to learn revisedand/or new processing parameter sets from the feature data, additionaldata, and metadata stored in the database 730. A variety of analyticaltechniques may be used to learn revised and/or new processing parametersets. These analytical techniques may include, for example, numericaland statistical analysis of feature data, additional data, and metadatasuch as location, date, and time metadata. These analytical techniquesmay include, for further example, semantic analysis of tags,descriptors, contexts, and other non-numerical metadata.

As an example of a learning process that may be performed at 740, someor all of the records in the database at 730 may be sorted into aplurality of clusters based according to feature data, location, tag ordescriptor, or some other factor. Some or all of these clusters mayoptionally be sorted into sub-clusters based on another factor. Whenrecords are sorted into clusters or sub-clusters based on non-numericalmetadata (e.g. tags or descriptors) semantic analysis may be used tocombine like metadata into a manageable number of clusters orsub-clusters. A consensus processing parameter set may then be developedfor each cluster or sub-cluster. For example, clear outliers may bediscarded and the consensus processing parameter set may be formed fromthe medians or means of processing parameters within the remainingprocessing parameter sets.

New or revised processing parameter sets learned and stored at 740 maybe transmitted to some or all of the plurality of personal audio systemsat 750. For example, new or recently revised processing parameter setsmay be pushed to some or all of the personal audio systems on anas-available basis, which is to say as soon as the new or recentlyrevised processing parameter sets are created. Processing parameterssets, including new and revised processing parameter sets may betransmitted to some or all of the personal audio systems atpredetermined periodic intervals, such as, for example, nightly, weekly,or at some other interval. Processing parameters sets, including new andrevised processing parameter sets may be transmitted upon request fromindividual personal audio systems. Processing parameter sets may bepushed to, or downloaded by, a personal audio system based on a changein the location of the personal audio system. For example, a personalaudio system that relocates to a position near or in an airport mayreceive one or more processing parameters sets for use suppressingaircraft noise.

FIG. 8 shows flow charts of methods 900 and 1000 for processing soundusing collective feedforward akin to those shown in FIG. 6.

The method 900 may start at 905 and run continuously until stopped (notshown). The actions at 910 to 950 are the same as the correspondingactions (610 to 650) in the process 600 as shown in FIG. 6. Descriptionsof these actions will not be repeated.

In response to detecting a trigger event (“yes” at 940), a determinationmay be made at 980 whether or not the most recent audio snippet containsspeech. Methods for speech detection are known to those of skill inaudio processing. When the most recent audio snippet contains speech(“yes” at 980), audio feature data may be extracted from the most recentaudio snippet as previously described. The extracted audio feature dataand associated metadata may be transmitted to the sound knowledgebase at970. When the most recent audio snippet does not contain speech (“no” at980), the most recent audio snippet and associated metadata to the soundknowledgebase at 990.

The actions 1010 to 1040 of the process 1000 are similar to thecorresponding actions within the process 700 shown in FIG. 6, with theexception that the process 1000 receives, stores, processes, and learnsfrom both audio feature data and audio snippets uploaded from personalaudio systems.

The overall process of learning new or revised processing parameter setsbased on audio snippets and metadata and providing those new or revisedprocessing parameter sets to personal audio systems is referred toherein as “collective feedforward”. The term “collective” indicates thenew or revised processing parameter sets are learned from the collectiveinputs from multiple personal audio systems. The term “feedforward” (incontrast to “feedback”) indicates new or revised processing parametersets are provided, or fed forward, to personal audio systems that maynot have contributed snippets and metadata to the creation of those newor revised processing parameter sets.

Information collected by the sound knowledgebase about how personalaudio systems are used in different locations, ambient soundenvironments, and situations may be useful for more than developing newor revised processing parameter sets. In particular, informationreceived from users of personal audio systems may indicate a degree ofsatisfaction with an ambient sound environment. For example, informationmay be collected from personal audio systems at a concert to gaugelistener satisfaction with the “house” sound. If all or a large portionof the personal audio systems were used to substantially modify thehouse sound, a presumption may be made that the audience (those with andwithout personal audio systems) was not satisfied. Information receivedfrom personal audio systems could be used similarly to gauge usersatisfaction with the sound and noise levels within stores, restaurants,shopping malls, and the like. Information received from personal audiosystems could also be used to create soundscapes or sound level mapsthat may be helpful, for example, for urban planning and traffic flowengineering.

Closing Comments

Throughout this description, the embodiments and examples shown shouldbe considered as exemplars, rather than limitations on the apparatus andprocedures disclosed or claimed. Although many of the examples presentedherein involve specific combinations of method acts or system elements,it should be understood that those acts and those elements may becombined in other ways to accomplish the same objectives. With regard toflowcharts, additional and fewer steps may be taken, and the steps asshown may be combined or further refined to achieve the methodsdescribed herein. Acts, elements and features discussed only inconnection with one embodiment are not intended to be excluded from asimilar role in other embodiments.

As used herein, “plurality” means two or more. As used herein, a “set”of items may include one or more of such items. As used herein, whetherin the written description or the claims, the terms “comprising”,“including”, “carrying”, “having”, “containing”, “involving”, and thelike are to be understood to be open-ended, i.e., to mean including butnot limited to. Only the transitional phrases “consisting of” and“consisting essentially of”, respectively, are closed or semi-closedtransitional phrases with respect to claims. Use of ordinal terms suchas “first”, “second”, “third”, etc., in the claims to modify a claimelement does not by itself connote any priority, precedence, or order ofone claim element over another or the temporal order in which acts of amethod are performed, but are used merely as labels to distinguish oneclaim element having a certain name from another element having a samename (but for use of the ordinal term) to distinguish the claimelements. As used herein, “and/or” means that the listed items arealternatives, but the alternatives also include any combination of thelisted items.

What is claimed:
 1. A system, comprising: a memory; and a processorconfigured to: receive audio feature data and associated metadata from apersonal audio system, wherein the audio feature data is extracted froma most recent snippet of an ambient audio stream; analyze the audiofeature data and the associated metadata; develop a current ambientsound profile based, at least in part, on the audio feature data;develop a new processing parameter set based, at least in part, on thecurrent ambient sound profile, the new processing parameter setindicating a type and degree of one or more processes to be performed onthe ambient audio stream; provide the new processing parameter set tothe personal audio system; and cause the memory to store the newprocessing parameter set and the current ambient sound profile.
 2. Thesystem of claim 1, wherein the processor is further configured toreceive the audio feature data in response to a trigger event associatedwith the personal audio system.
 3. The system of claim 2, wherein thetrigger event is based on a location of the personal audio system. 4.The system of claim 3, wherein the trigger event involves the automaticretrieval, based on location or ambient sound profile, of a selectedprocessing parameter set for use as an active processing parameter set.5. The system of claim 1, wherein the associated metadata includes anactive processing parameter set before modification.
 6. The system ofclaim 1, wherein the associated metadata transmitted to the deviceincludes a descriptor received from a user via a user interface.
 7. Thesystem of claim 1, wherein the associated metadata transmitted to thedevice includes data indicating a location of the personal audio system.8. The system of claim 1, wherein the audio feature data includes one ormore of arithmetic mean, geometric mean, harmonic mean, centroid,variance, standard deviation, spectral skew, kurtosis, spectral shape,rolloff, spread, flatness, spectral flux, Mel frequency cepstralcoefficients, pitch, tonal power ratio, harmonic-to-average power ratio,maximum of autocorrelation function, zero crossing rate, RMS power, peakpower, crest factor, amplitude/power envelope and attack/decay rates. 9.The system of claim 1, wherein the processor is configured to developthe new processing parameter set using one or more machine learningtechniques.
 10. The system of claim 1, wherein the processor isconfigured to develop the new processing parameter set using one or moreanalytical techniques.
 11. The system of claim 1, wherein to develop anew processing parameter set includes: sorting the audio feature dataand associated metadata from the personal audio system and additionalaudio feature data and associated metadata from one or more otherpersonal audio systems into a plurality of clusters; and developing acorresponding consensus processing parameter set for each cluster of theplurality of clusters.
 12. The system of claim 11, wherein the pluralityof clusters are sorted based on the associated metadata from thepersonal audio system and the associated metadata from the otherpersonal audio systems.
 13. The system of claim 11, wherein thecorresponding consensus processor parameter set is based on a median oraverage of processing parameters associated with a cluster.
 14. Thesystem of claim 11, wherein an outlier processing parameter is discardedfrom a cluster.
 15. A method, comprising: receiving audio feature dataand associated metadata from a personal audio system, wherein the audiofeature data is extracted from a most recent snippet of an ambient audiostream; analyzing the audio feature data and the associated metadata;developing a current ambient sound profile based, at least in part, onthe audio feature data; developing a new processing parameter set based,at least in part, on the current ambient sound profile, the newprocessing parameter set indicating a type and degree of one or moreprocesses to be performed on the ambient audio stream; and providing thenew processing parameter set to the personal audio system.
 16. Themethod of claim 15, further comprising receiving the audio feature datain response to a trigger event associated with the personal audiosystem.
 17. The method of claim 15, wherein developing a new processingparameter set includes: sorting the audio feature data and associatedmetadata from the personal audio system and additional audio featuredata and associated metadata from one or more other personal audiosystems into a plurality of clusters; and developing a correspondingconsensus processing parameter set for each cluster of the plurality ofclusters.
 18. The method of claim 17, further comprising providing thenew processing parameter set to one or more other personal audiosystems.
 19. The method of claim 17, wherein the corresponding consensusprocessor parameter set is based on a median or average of processingparameters associated with a cluster.
 20. One or more non-transitorymedia having software stored thereon, the software includinginstructions for controlling one or more devices for: receiving audiofeature data and associated metadata from a personal audio system,wherein the audio feature data is extracted from a most recent snippetof an ambient audio stream; analyzing the audio feature data and theassociated metadata; developing a current ambient sound profile based,at least in part, on the audio feature data; developing a new processingparameter set based, at least in part, on the current ambient soundprofile, the new processing parameter set indicating a type and degreeof one or more processes to be performed on the ambient audio stream;and providing the new processing parameter set to the personal audiosystem.